Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

[Extract] Population based optimization algorithms are the techniques which are in the set of the nature based optimization algorithms. The creatures and natural systems which are working and developing in nature are one of the interesting and valuable sources of inspiration for designing and inventing new systems and algorithms in different fields of science and technology. Evolutionary Computation (Eiben& Smith, 2003), Neural Networks (Haykin, 99), Time Adaptive Self-Organizing Maps (Shah-Hosseini, 2006), Ant Systems (Dorigo & Stutzle, 2004), Particle Swarm Optimization (Eberhart & Kennedy, 1995), Simulated Annealing (Kirkpatrik, 1984), Bee Colony Optimization (Teodorovic et al., 2006) and DNA Computing (Adleman, 1994) are among the problem solving techniques inspired from observing nature. In this chapter population based optimization algorithms have been introduced. Some of these algorithms were mentioned above. Other algorithms are Intelligent Water Drops (IWD) algorithm (Shah-Hosseini, 2007), Artificial Immune Systems (AIS) (Dasgupta, 1999) and Electromagnetism-like Mechanisms (EM) (Birbil & Fang, 2003). In this chapter, every section briefly introduces one of these population based optimization algorithms and applies them for solving the TSP. Also, we try to note the important points of each algorithm and every point we contribute to these algorithms has been stated. Section nine shows experimental results based on the algorithms introduced in previous sections which are implemented to solve different problems of the TSP using well-known datasets

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Optimization of robotic assembly of printed circuit board by using evolutionary algorithm

This research work describes the development and evaluation of a custom application exploring the use of Artificial Immune System algorithms (AIS) to solve a component placement sequencing problem for printed circuit board (PCB) assembly. In the assembly of PCB’s, the component placement process is often the bottleneck and the equipment to complete component placement is often the largest capital investment

Evaluating and developing parameter optimization and uncertainty analysis methods for a computationally intensive distributed hydrological model

This study focuses on developing and evaluating efficient and effective parameter calibration and uncertainty methods for hydrologic modeling. Five single objective optimization algorithms and six multi-objective optimization algorithms were tested for automatic parameter calibration of the SWAT model. A new multi-objective optimization method (Multi-objective Particle Swarm and Optimization & Genetic Algorithms) that combines the strengths of different optimization algorithms was proposed. Based on the evaluation of the performances of different algorithms on three test cases, the new method consistently performed better than or close to the other algorithms. In order to save efforts of running the computationally intensive SWAT model, support vector machine (SVM) was used as a surrogate to approximate the behavior of SWAT. It was illustrated that combining SVM with Particle Swarm and Optimization can save efforts for parameter calibration of SWAT. Further, SVM was used as a surrogate to implement parameter uncertainty analysis fo SWAT. The results show that SVM helped save more than 50% of runs of the computationally intensive SWAT model The effect of model structure on the uncertainty estimation of streamflow simulation was examined through applying SWAT and Neural Network models. The 95% uncertainty intervals estimated by SWAT only include 20% of the observed data, while Neural Networks include more than 70%. This indicates the model structure is an important source of uncertainty of hydrologic modeling and needs to be evaluated carefully. Further exploitation of the effect of different treatments of the uncertainties of model structures on hydrologic modeling was conducted through applying four types of Bayesian Neural Networks. By considering uncertainty associated with model structure, the Bayesian Neural Networks can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation

Linear-dynamic model of the problem of optimization of placement and alternation of crops in cropping rotation

The problem of determining the best positioning and rotation of crops within fields of crop rotation has been approached through two different methods, static and dynamic. In the static model, specific schemes are chosen based on the predetermined number and size of rotation fields that can accommodate a particular crop. The model takes into consideration various factors such as crop yields within the same field, cotton production volume, labor costs, production expenses, and conditional net income. The optimization criterion in this model is to minimize production costs and maximize conditional net income, which is the difference between the costs of gross output per hectare and production expenses per hectare. The initial data is computed using long-term farm data, and information is prepared for each crop rotation scheme to enable a comprehensive evaluation of all crop rotation schemes and fields while taking into account major indicators of farm production

Apex and fuzzy model assessment of environmental benefits of agroforestry buffers for claypan soils

Contamination of surface waters with pollutants from agricultural land is a major threat to the environment. A field-size watershed study in Northeast Missouri showed that vegetated filter strips containing grass and grass+trees (agroforestry) buffers placed on contours reduced sediment and nutrient loadings by 11-35%. Watershed scale studies are overly expensive while computer simulated hydrologic models offer efficient and economical tools to examine environmental benefits of conservation practices. The current study used the Agricultural Policy Environmental eXtender (APEX) model and a fuzzy logic model to predict environmental benefits of buffers and grass waterways of three adjacent watersheds at the Greenley Memorial Research Center. During the second phase of the study, an automated computer technique was developed to optimize parameter sets for the APEX model for runoff, sediment, total phosphorous (TP) and total nitrogen (TN) losses. The APEX model was calibrated and validated satisfactorily for runoff from both pre- and post-buffer watersheds. The sediment, TP, and TN were calibrated only for larger events during the pre-buffer period (>50 mm). Only TP was calibrated by post-buffer models. The models simulated 13- 25% TP reduction by grass waterways, and 4-5% runoff and 13-45% TP reductions by buffers. The fuzzy model predicted runoff for the study watersheds and for watersheds 30 and 50 times larger in northern Missouri. A stepwise multi-objective, multi-variable parameter optimization technique improved calibration of sediments, TP, and TN after optimization for runoff parameters. The results of the study show that models can be used to examine environmental benefits provided long-term data are available